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Record W1808222962 · doi:10.1021/acsami.5b08630

High-Energy Faceted SnO<sub>2</sub>-Coated TiO<sub>2</sub> Nanobelt Heterostructure for Near-Ambient Temperature-Responsive Ethanol Sensor

2015· article· en· W1808222962 on OpenAlexaff
Guohui Chen, Shaozheng Ji, Haidong Li, Xueliang Kang, Sujie Chang, Yana Wang, Guangwei Yu, Jianren Lu, Jérôme P. Claverie, Yuanhua Sang, Hong Liu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversité du Québec à Montréal
FundersMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMaterials scienceHeterojunctionHydrothermal circulationChemical engineeringChemisorptionEpitaxyYield (engineering)EthanolNanotechnologyOptoelectronicsCatalysisComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A SnO2 gas sensor was prepared by a two-step oxidation process whereby a Sn(II) precursor was partially oxidized by a hydrothermal process and the resulting Sn3O4 nanoplates were thermally oxidized to yield SnO2 nanoplates. The SnO2 sensor was selective and responsive toward ethanol at a temperature as low as 43 °C. This low sensing temperature stems from the rapid charge transport within SnO2 and from the presence of high-energy (001) facets available for oxygen chemisorption. SnO2/TiO2 nanobelt heterostructures were fabricated by a similar two-step process in which TiO2 nanobelts acted as support for the epitaxial growth of intermediate Sn3O4. At temperatures ranging from 43 to 276 °C, the response of these branched nanobelts is more than double the response of SnO2 for ethanol detection. Our observations demonstrate the potential of low-cost SnO2-based sensors with controlled morphology and reactive facets for detecting gases around room temperature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.202
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations84
Published2015
Admission routes1
Has abstractyes

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